Mo Han
Impact in
- Applied Mathematics top 10%
- Mathematical Analysis and Transform Methods
-
- Motor Control and Adaptation
- EEG and Brain-Computer Interfaces
Papers in
-
- Muscle activation and electromyography studies 6
-
- EEG and Brain-Computer Interfaces 7
- Co-authors
- S. Arimoto (1 shared paper)Kenji Tahara (1 shared paper)Jun Shi (3 shared papers)Xiaoping Liu (2 shared papers)Deniz Erdoğmuş (8 shared papers)Naitong Zhang (2 shared papers)Lei He (1 shared paper)Qingzhong Li (1 shared paper)
- Journals
- IEEE Transactions on Signal Processing (2 papers)Scientific Reports (1 paper)Robotica (1 paper)Journal of Orthopaedic Research® (1 paper)Frontiers in Neuroscience (1 paper)
- Partner nations
- United StatesChinaMexico
In The Last Decade
Mo Han
17 papers receiving 272 citations
Peers
Comparison fields: 5 of 53
- Applied Mathematics 60
- Cognitive Neuroscience 87
- Signal Processing 47
- Control and Systems Engineering 93
- Computer Vision and Pattern Recognition 68
Countries citing papers authored by Mo Han
This map shows the geographic impact of Mo Han's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Mo Han with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mo Han more than expected).
Fields of papers citing papers by Mo Han
This network shows the impact of papers produced by Mo Han. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Mo Han. The network helps show where Mo Han may publish in the future.
Co-authors
The 25 scholars most cited alongside Mo Han, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2001 | 101 | |
| 2 | 2016 | 46 | |
| 3 | 2020 | 30 | |
| 4 | 2020 | 19 | |
| 5 | 2019 | 16 | |
| 6 | 2024 | 14 | |
| 7 | 2016 | 12 | |
| 8 | 2021 | 10 | |
| 9 | 2018 | 6 | |
| 10 | 2022 | 5 | |
| 11 | 2024 | 5 | |
| 12 | 2019 | 4 | |
| 13 | 2020 | 3 | |
| 14 | 2025 | 2 | |
| 15 | 2024 | 2 | |
| 16 | 2018 | 2 | |
| 17 | 2021 | 2 | |
| 18 | 2024 | 0 | |
| 19 | 2025 | 0 |
About Mo Han
Mo Han is a scholar working on Biomedical Engineering, Cognitive Neuroscience, Computer Vision and Pattern Recognition, Cellular and Molecular Neuroscience and Cardiology and Cardiovascular Medicine, having authored 19 papers that have together received 279 indexed citations. Recurring topics across this work include EEG and Brain-Computer Interfaces (7 papers), Muscle activation and electromyography studies (6 papers), Neuroscience and Neural Engineering (3 papers), Image and Signal Denoising Methods (3 papers), Mathematical Analysis and Transform Methods (3 papers), Robot Manipulation and Learning (2 papers), Emotion and Mood Recognition (2 papers) and Digital Filter Design and Implementation (2 papers). The work is most often cited by research in Applied Mathematics (60 citations), Cognitive Neuroscience (87 citations), Signal Processing (47 citations), Control and Systems Engineering (93 citations) and Computer Vision and Pattern Recognition (68 citations). Mo Han has collaborated with scholars based in United States, China and Mexico. Frequent co-authors include S. Arimoto, Kenji Tahara, Jun Shi, Xiaoping Liu, Deniz Erdoğmuş, Naitong Zhang, Lei He, Qingzhong Li, Ye Wang and Qinyu Zhang. Their work appears in journals such as IEEE Transactions on Signal Processing, Scientific Reports, Robotica, Journal of Orthopaedic Research® and Frontiers in Neuroscience.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.